Tag Archives: in-store tracking

Not too long ago I spoke in Toronto at a Symposium focused on Machine Learning to describe what we’ve done and are trying to do with Machine Learning (ML) in our DM1 platform and with store analytics in general. Machine Learning is, in some respects, a fraught topic these days. When something is hard on the hype cycle, the tendency is to either believe it’s the answer to every problem or to dismiss the whole thing as an illusion. The first answer is never right. The second sometimes is. But ML isn’t an illusion – it’s a real capability with a fair number of appropriate applications. I want to cover – from our hands-on, practical perspective – where we’ve used ML, why we used ML and show a case-study of some of the results.

Just what is Machine Learning?

In its most parochial form, ML is really nothing more than a set of (fairly mature) statistical techniques dressed up in new clothes.

Here’s a wonderful extract from the class notes of a Stanford University expert on ML: (http://statweb.stanford.edu/~tibs/stat315a/glossary.pdf)

It’s pretty clear why we should all be talking ML not statistics! And seriously, wasn’t data science enough of a salary upgrade for statisticians without throwing ML into the hopper?

Unlike big data, I have no desire in this case to draw any profound definitional difference between ML and statistics. In my mind, I think of ML as being the domain of neural networks, deep learning and Support Vector Machines (SVMs). Statistics is the stuff we all know and love like regression and factor analysis and p values. That’s a largely ad hoc distinction (and it’s particularly thin on the unsupervised learning front), but I think it mostly captures what people are thinking when they talk about these two disciplines.

Data quality is by far the least sexy of these applications, but it’s also the area where we’ve done the most work and where the everyday application of our platform takes actual advantage of some ML work.

When we setup a client instance on DM1, there’s a number of highly specific configurations that control how data gets processed. These configurations help guide the platform in key tasks like distinguishing Associate electronic devices from shopper devices. Why is this so important? Well, if you confuse Associates with shoppers, you’ll grossly over-count shoppers in the store. Equally bad, you’ll miss out on a real treasure trove of Associate data including when Associate/Shopper interactions occur, the ratio of Shoppers to Associates (STARs), and the length and outcome from interactions. That’s all very powerful.

If you identify store devices, it’s easy enough to signature them in software. But we wanted a system that would do the same work without having to formally identify store devices. Not only does this make it a lot easier to setup a store, it fixes a ton of compliance issues. You may tell Associates not to carry their own devices on the floor, but if you think that rule is universally followed your kidding yourself. So even if you BLE badge employees, you’re still likely picking up their personal phones as shopper devices. By adding behavioral identification of Associates, we make the data better and more accurate while minimizing (in most cases removing) operational impact.

We use a combination of rule-based logic and ML to classify Associate behavior on ALL incoming devices. It turns out that Associates behave quite differently in stores than shoppers. They spend more time. Go places shoppers can’t. Show up more often. Enter at different times. Exit at different times. They’re different. Some of those differences are easily captured in simple IF-then programming logic – but often the patterns are fairly complex. They’re different, but not so easily categorized. That’s where the ML kicks in.

We also work in a lot of electronically dense environments. So we not only need to identify Associates, we need to be able to pick-out static devices (like display computers, endless aisle tablets, etc.). That sounds easy, and in fact it is fairly easy. But it’s not quite as trivial as it sounds; given the vagaries of positioning tech, a static device is never quite static. We don’t get the same location every time – so we have to be able to distinguish between real movement and the type of small, Brownian motion we get from a static device.

Fixing data quality is never all that exciting, but in the world of shopper journey measurement it’s essential. Without real work to improve the data – work that ML happens to be appropriate for – the data isn’t good enough.

The second use we’ve found for machine learning is in shopper classification. We’re building a generalized shopper segmentation capability into the next release of DM1. The idea is pretty straightforward. For years, I’ve championed the notion of 2-tiered segmentation in digital analytics. That’s just a fancy name for adding a visit-type segmentation to an existing customer segmentation. And the exact same concept applies to stores.

As consultants, we typically built highly customized segmentation schemes. Since Digital Mortar is a platform company, that’s not a viable approach for us. Instead, what we’ve done is taken a set of fairly common in-store behavioral patterns and generalized their behavioral signatures. These patterns include things like “Clearance Shoppers”, “Right-Rail Shoppers”, “Single Product Focused Shoppers”, “Product Returners”, and “Multi-Product Browsers”. By mapping store elements to key behavior points, any store can then take advantage of this pre-existing ML-driven segmentation.

It’s pretty cool stuff and I’m excited to get it into the DM1 platform.

The last problem we’ve tackled with ML is finding optimal store paths. This one’s more complex – more complex than we’ve been comfortable taking on directly. We have a lot of experience in segmentation techniques – from cluster analysis to random forests to SVMs. We’re pretty comfortable with that problem set. But for optimal path analysis, we’ve been working with DXi. They’re an ML company with a digital heritage and a lot of experience working on event-level digital data. We’ve always said that a big part of what drew us to store journey measurement is how similar the data is to digital journey data and this was a chance to put that idea to the test. We’ve given them some of our data and had them work on some optimal path problems – essentially figuring out whether the store layout is as good as possible.

Why use a partner for this? I’ve written before about how I think Digital Mortar and the DM1 platform fit in a broader analytics technology stack for retail. DM1 provides a comprehensive measurement system for shopper tracking and highly bespoke reporting appropriate to store analytics. It’s not meant to be a general purpose analytics platform and it’s never going to have the capabilities of tools like Tableau or R or Watson. Those are super-powerful general-purpose analytics tools that cover a wide range of visualization, data exploration and analytic needs. Instead of trying to duplicate those solutions we’ve made it really easy (and free) to export the event level data you need to drive those tools from our platform data.

I don’t see DM1 becoming an ML platform. As analysts, we’ll continue to find uses for ML where we think it’s appropriate and embed those uses in the application. But trying to replicate dedicated ML tools in DM1 just doesn’t make a lot of sense to me.

In my next post, I’ll take a deeper dive into that DXi work, give a high-level view of the analytics process, and show some of the more interesting results.

When we released the first full production version of DM1 in May, it was a transformational leap in customer location analytics. Now, six months later, we’re releasing the first major upgrade to DM1 – and it’s a doozy. We’re adding powerful new approaches to improving the store and driving actionable results while putting the competition even further in the rear-view mirror (they’re back there somewhere). Some of the coolest new features include:

Real-time View: See and track what’s happening in the store as it happens

Real-time Messaging: Integrate with Associate and Shopper Mobile devices based on what’s happening right now

Playback: See what happened in the store using a time-lapse simulation of actual shopper behavior

Week-Time: A cool new visualization that shows a Day-Time part analysis for ANY metric or segment

Flexible Cloud Hosting: Azure or Google Cloud, your choice.

And that’s just the big stuff. There have been a host of small tweaks, fixes, performance enhancements and visual improvements in the intervening months.

That initial version of DM1 had a pretty remarkable feature set for a V1 product in a brand new market. It delivered a revolutionary store visualization tool that mapped its powerful metrics into the store at any level of abstraction – from Display to Section to Department to Floor to Store. And the metric set if provided shattered existing door-counting paradigms by delivering real journey metrics. What shoppers did first. Where they spent the most time. Every place they shopped. What got the most consideration. What converted. What didn’t. And DM1 V1 integrated a rich set of Associate tracking metrics into the basic product as well. Associate presence, intra-day STARs, interaction rates and times, and associated lift. DM1 also delivered powerful grid-based reporting and charting, a really cool funnel analytics tool and a powerful side-by-side comparison tool.

With the new features, we’ve focused on adding a set of capabilities that extend DM1 deeper into the store and make it easier to integrate into broader store marketing and operations efforts. The biggest part of that is, of course, real-time.

We’ve always collected store data in real-time – and all of our collection technologies support near real-time data about the shopper. But in the V1 release, everything we did was batch processing and next-day analytics. That isn’t just because batch processing is easier (though that did matter to us). It was also because many years of experience in digital analytics convinced us that the applications for real-time analytics were fairly rare. There was a time when real-time reporting was a huge part of the digital analytics sales process. But in the end, the dominant tools in digital analytics deliver intra-day but far from real-time analytics. So we figured if a very mature market like digital analytics could do without real-time, we didn’t need it for our V1 release.

But the store is different beast than a Website, and we’re finding that having a real-time view of what’s happening makes it possible to add value at the Store Manager level AND support both CRM and queue management applications that help improve the customer experience.

So real-time became a central piece of our V2 release. And once we built out the core capability, we took advantage of it in multiple ways.

The Realtime view show’s what’s happening right now in the store. Associates and Shoppers are shown (using different symbols) and you can quite literally track exactly where they are and where they are going. We even color-code Shoppers to make it easy to identify how long they’ve been in the store.

Check it out:

Pretty cool. The Playback capability provides the same view but processes historical data. Since it’s historical, it can collapse time periods into a time-lapse view. So you can see an hour or a day in five minutes.

Being able to see real-time data is cool and highly useful for folks on the ground, but I won’t claim the analytic implications are enormous. What is enormous, though, is the capability to tie DM1’s tracking and measurement into real-time messaging systems. We’ve built a straightforward Webhook messaging interface that lets you get three kinds of data out of DM1: current metric data (for uses like queue length), real-time shopper data, and real-time Associate data.

The real-time shopper data can be based off any customer key we have. And it provides a CRM view that includes Entry Time, Total-Time in Store, Presence and Lingers by Section, Current Location, Time since Interaction, and Associate ID of last interaction. You can use this data to generate real-time messages via your mobile application or in-store beacons to the shopper.

As cool as this shopper interface is, I’ve long been a believer that messaging Associates is even more fundamental and important. With shoppers, opt-in is always a challenge. And barring a truly compelling application, I think it’s tough to get enough opt-ins for messaging to make it impactful. But Associates are increasingly being armed with digital tools that allow them to do more (like distributed PoS) and serve the customer better. Being able to optimize Associate interactions with real-time data and positioning is a huge leap forward in operational sophistication.

In addition to real-time, we’ve made a boatload of analytic enhancements too. And one of my favorite new views is the WeekTime report. We’d already build a custom report around staffing that laid out STARs (Shopper to Associate Ratio) by day of week and time of day (down to the hour). But that report wasn’t a thing of beauty and, in any case, it was specific to the STARs metric.

Because Day-time parting is so fundamental to the store, we wanted a generalized analytics tool that would do the job. The WeekTime tool lays out any metric by day-of-week and time-of-day.

Here are some examples of the WeekTime tool. The first view shows when the store had the most shoppers. The second view shows when shoppers spent the most time in the store.

Shopper Traffic by Day-Time

Avg. Shop Time by Day-Time

And like all DM1 workbench tools, the Weektime tools is driven by our big data event-level engine so it supports integrated segmentation across any time-period. It’s a really nice analytic addition to the Workbench and the Dashboard view. It makes it much easier to quickly visualize and understand how day-time parts are driving performance on any measurement.

One of the biggest changes we made in V2 isn’t functional but environmental. We built DM1 on Azure and it’s been a great platform. But we’ve seen that our clients are going in all sorts of directions in an incredibly competitive cloud marketplace. And if the rest of your infrastructure is on Google Cloud (GCP), then it just makes sense that DM1 live there too. So in V2, we offer the choice of cloud provider. We’ve ported the entire platform into GCP and – as a bonus gift to ourselves – made the deployment process a lot more automated and easier for us. Microsoft Azure or Google GCP – it’s now your choice. And it’s just part of making sure DM1 is the most technologically sophisticated AND seamless platform around.

V2 is another big step for us. But we’re just getting started. I keep an ever-growing Trello board of new features and some of the most exciting stuff to come includes a full real-time Store Manager tool, a much more comprehensive and beautiful Associate performance report, a store-change report that automatically shows you the impact of every store change in a period of time, the integration of D3 into our charting capability, a full pathing tool, a robust segmentation builder, and even an initial foray into machine learning.

In my last few posts, I explained what in-store journey analytics is, described the basics of the technology and the data collection used, and went into some detail about its potential business uses. Throughout, and especially in that last part around business uses, I wrote on the assumption that this type of measurement is all about retail stores. After all, brick & mortar stores are the primary focus of Digital Mortar AND of nearly every company in the space. But here’s the thing, this type of measurement is broadly applicable to a wide variety of applications where customer movement though a physical environment is a part of the experience. Stadiums, malls, resorts, cruise ships, casinos, events, hospitals, retail banks, airports, train stations and even government buildings and public spaces can all benefit from understanding how physical spaces can be optimized to drive better customer or user experiences.

In these next few posts, I’m going to step outside the realm of stores and talk about the opportunities in the broader world for customer journey tracking. I’ll start by tackling some of the differences between the tracking technologies and measurement that might be appropriate in some of these areas versus retail, and then I’m going to describe specific application areas and delve a little deeper into how the technology might be used differently than in traditional retail. While the underlying measurement technology can be very similar, the type of reporting and analytics that’s useful to a stadium or resort is different than what makes sense for a mall store.

Since I’m not going to cover every application of customer journey tracking outside retail in great detail, I’ll start with some general principles of location measurement based upon industry neutral things like the size of the space and the extent to which the visitors will opt-in to wifi or use an app.

Measuring BIG Spaces versus little ones

With in-store journey tracking, you have three or four alternatives when choosing the underlying measurement collection technology. Cameras, passive wifi, opt-in wifi and bluetooth, and dedicated sniffers are all plausible solutions. With large spaces like stadiums and airports, it’s often too expensive to provide comprehensive camera coverage. It can even be too expensive to deploy custom measurement devices (like sniffers). That’s especially true in environments where the downtime and wiring costs can greatly exceed the cost of the hardware itself.

So for large spaces, wifi tracking often becomes the only realistic technology for deploying a measurement system. That’s not all bad. While out-of-the-box wifi is the least accurate measurement technology, most large spaces don’t demand fine-grained resolution. In a store, a 3 meter circle of error might place a customer in a completely different section of the store. In an airport, it’s hard to imagine it would make much difference.

Key Considerations Driven by Size of Location:

How much measurement accuracy to do you need?

How expensive will measurement specific equipment and installation be and is it worth the cost?

Are there special privacy considerations for your space or audience?

Opt-in vs. Anonymous Tracking

Cameras, passive wifi and sniffers can all deliver anonymous tracking. Wifi, Bluetooth and mobile apps all provide the potential for opt-in tracking. There are significant advantages to opt-in based tracking. First, it’s more accurate. Particularly in out-of-the-box passive wifi, the changes in IoS to randomize MAC addresses have crippled straightforward measurement and made reasonably accurate customer measurement a challenge. When a user connects to your wifi or opens an app, you can locate them more frequently and more precisely and their phone identity is STABLE so you can track them over time. If your primary interest is in understanding specific customers better for your CRM, tracking over-time populations or you have significant issues with the privacy implications of anonymized passive tracking, then opt-in tracking is your best bet. However, this choice is dependent on one further fact: the extent to which your customers will opt-in. For stadiums and resorts, log-in rates are quite high. Not so much at retail banks. Which brings us to…

Key Considerations for Opt-In Based Tracking

Will a significant segment of your audience opt-in?

Are you primarily interested in CRM (where opt-in is critical) or in journey analytics (which can be anonymous)?

How good is the sample?

Some technologies (like camera) provide comprehensive coverage by default. Most other measurement technologies inherently take some sample. Any form of signal detection will start with a sample that includes only people with phones. That isn’t much of a sample limitation though it will exclude most smaller children. Passive methods further restrict the population to people with wifi turned on. Most estimates place the wifi-activated rate at around 80%. That’s a fairly high number and it seems unlikely that this factor introduces significant sample bias. However, when you start factoring in things like Android user or App downloader or wifi user, you’re often introducing significant reductions in sample size AND adding sample biases that may or may not be difficult to control for. App users probably aren’t a representative sample of, for example, the likelihood of a shopper to convert in a store. But even if they are a small percentage of your total users, they are likely perfectly representative of how long people spend queuing in lines at a resort. One of the poorly understood aspects of measurement science is that the same sample can be horribly biased for some purposes but perfectly useful for others!

Key Considerations for Sampling

Does your measurement collection system bias your measurement in important ways?

Are people who opt-in a representative sample for your measurement purposes?

The broad characteristics that define what type of measurement system is right for your needs are, of course, determined by what questions you need to answer. I’ll take a close look at some of the business questions for specific applications like sports stadiums next time. In general, though, large facilities by their very nature need less fine-grained measurement than smaller ones. For most applications outside of retail, being able to locate a person within a 3 meter circle is perfectly adequate. And while the specific questions being answered are often quite specific to an application area, there is a broad and important divide between measurement that’s primarily focused on understanding patterns of movement and analysis that’s focused on understanding specific customers. When your most interested in traffic patterns, then samples work very well. Even highly biased samples will often serve. If, on the other hand, you’re looking to use customer journey tracking to understand specific customers or customer segments (like season-ticket holders) better, you should focus on opt-in based techniques. In those situations, identification trumps accuracy.

If you have questions about the right location-based measurement technology solution for your business, drop us a line at info@digitalmortar.com

Next up, I’ll tackle the surprisingly interesting world of stadium/arena measurement.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.